Computational Identification of Aging Related Drugs

Abstract

Introduction: Aging is a complex multifaceted process that involves interactions between many biochemical and cellular mechanisms. Aging is the main cause of most of the important chronic diseases. Using computational methods to search for drugs affecting known components and processes in human aging has been an efficient approach, as it has been able to rediscover several drugs known to extend lifespan in animal models. Materials and methods: In the present study, first human genes related to aging were extracted and then proteins related to these genes were collected. Then the gene-protein matrix was drawn for the collected data. In the next step, using DINIES, which is a drug-target interaction network inference engine based on supervised analysis, the drugs that were associated with these proteins and genes were obtained and their interaction network was drawn. Then the desired thresholds were applied to identify drugs. Finally, by obtaining drugs related to aging for model organisms, drugs that are effective in the aging of model organisms were also obtained. Findings: Based on the findings of this study, the use of computational methods toidentify drugs that are related to aging are both highly accurate and economical in terms of time and cost. Conclusion: In aging studies, due to ethical considerations and the time-consuming nature of examining the effects of drugs and chemical compounds on the mechanisms of aging and lifespan in humans, the need for reliable and fast techniques to identify drugs is felt. With computational techniques, it is possible to create a revolution in the system of designing and identifying drugs, as well as preventing and dealing with many diseases. In this study, we have reached some drugs related to aging, including caffeine and digoxin.